Count and Summarize
Last updated on 2025-06-24 | Edit this page
Estimated time: 25 minutes
Overview
Questions
- How does
count()help in counting categorical data? - How do you compute summary statistics?
- How can you use
group_by()to compute summary statistics for specific subgroups? - Why is it useful to combine
count()withfilter()? - How can you compute relative frequencies using
group_by()andmutate()?
Objectives
- Understand how to use
count()to summarize categorical variables. - Learn to compute summary statistics using
summarize(). - Explore how
group_by()allows for subgroup analyses. - Use
filter()to refine data before counting or summarizing. - Compute and interpret relative frequencies using
mutate().
Computing Summaries
This lesson will teach you some of the last (and most important)
functions used in the process of data exploration and data analysis.
Today we will learn how to quickly count the number of appearances for a
given value using count() and how to compute custom summary
statistics using summarize().
Taken together, these functions will let you quickly compute frequencies and average values in your data, the last step in descriptive analysis and sometimes a necessary prerequisite for inference methods. The ANOVA, for example, most often requires one value per participant per condition, often requiring you to average over multiple trials.
R
library(dplyr)
OUTPUT
Attaching package: 'dplyr'OUTPUT
The following objects are masked from 'package:stats':
filter, lagOUTPUT
The following objects are masked from 'package:base':
intersect, setdiff, setequal, unionR
dass_data <- read.csv("data/kaggle_dass/data.csv")
Counting data
Let’s start with the more simple function of the two,
count(). This function works with the
tidyverse framework and can thus easily be used with pipes.
Simply pipe in your dataset and give the name of the column you want to
receive a count for:
R
dass_data %>%
count(education)
OUTPUT
education n
1 0 515
2 1 4066
3 2 15066
4 3 15120
5 4 5008The count will always be in a column called n, including
the number of occurrences for any given value (or combination of values)
to the left of it.
Notably, you can also include multiple columns to get a count for each combination of values.
R
dass_data %>%
count(hand, voted)
OUTPUT
hand voted n
1 0 0 4
2 0 1 51
3 0 2 118
4 1 0 284
5 1 1 9693
6 1 2 24765
7 2 0 36
8 2 1 1121
9 2 2 3014
10 3 0 3
11 3 1 183
12 3 2 503Tip: Using comparisons in
count()
You are not limited to comparing columns. You can also directly
include comparisons in the count() function. For example,
we can count the education status and number of people who concluded the
DASS-questionnaire in under 10 minutes.
R
dass_data %>%
count(testelapse < 600, education)
OUTPUT
testelapse < 600 education n
1 FALSE 0 33
2 FALSE 1 220
3 FALSE 2 1025
4 FALSE 3 922
5 FALSE 4 333
6 TRUE 0 482
7 TRUE 1 3846
8 TRUE 2 14041
9 TRUE 3 14198
10 TRUE 4 4675If you want to receive counts only for a specific part of the data,
like in our case people faster than 10 minutes, it may be more clear to
filter() first.
R
dass_data %>%
filter(testelapse < 600) %>%
count(education)
OUTPUT
education n
1 0 482
2 1 3846
3 2 14041
4 3 14198
5 4 4675Summarize
summarize() is one of the most powerful functions in
dplyr. Let’s use it to compute average scores for the
DASS-questionnaire.
First, compute columns with the sum DASS score and mean DASS score for each participant again. You should have code for this in the script from last episode. Go have a look and copy the correct code here. Copying from yourself is highly encouraged in coding! It’s very likely that you have spent some time solving a problem already, why not make use of that work.
Hint: Copy your own code
If you can’t find it or want to use the exact same specification as me, here is a solution.
R
dass_data <- dass_data %>%
mutate(
sum_dass_score = rowSums(across(starts_with("Q") & ends_with("A"))),
mean_dass_score = rowMeans(across(starts_with("Q") & ends_with("A")))
)
summarize() works similar to mutate() in
that you have to specify a new name and a formula to receive your
result. To get the average score of the DASS over all participants, we
use the mean() function.
R
dass_data %>%
summarize(
mean_sum_dass = mean(sum_dass_score)
)
OUTPUT
mean_sum_dass
1 100.2687We can also compute multiple means over both the column containing the sum of DASS scores and the mean DASS score.
R
dass_data %>%
summarize(
mean_sum_dass = mean(sum_dass_score),
mean_mean_dass = mean(mean_dass_score)
)
OUTPUT
mean_sum_dass mean_mean_dass
1 100.2687 2.387351In addition, we can compute other values using any functions we feel
are useful. For example, n() gives us the number of values,
which can be useful when comparing averages for multiple groups.
R
dass_data %>%
summarize(
mean_sum_dass = mean(sum_dass_score),
mean_mean_dass = mean(mean_dass_score),
n_subjecs = n(),
max_total_dass_score = max(sum_dass_score),
quantile_5_mean_dass = quantile(mean_dass_score, 0.05),
quantile_95_mean_dass = quantile(mean_dass_score, 0.95)
)
OUTPUT
mean_sum_dass mean_mean_dass n_subjecs max_total_dass_score
1 100.2687 2.387351 39775 168
quantile_5_mean_dass quantile_95_mean_dass
1 1.261905 3.595238Groups
So far, we could have done all of the above using basic R functions
like mean() or nrow(). One of the benefits of
summarize() is that it can be used in pipelines, so you
could have easily applied filters before summarizing. However, the most
important benefit is the ability to group the summary by other columns
in the data. In order to do this, you need to call
group_by().
This creates a special type of data format, called a grouped data
frame. R remembers assigns each row of the data a group based on
the values provided in the group_by() function. If you then
use summarize(), R will compute the summary functions for
each group separately.
R
dass_data %>%
group_by(education) %>%
summarize(
mean_mean_dass_score = mean(mean_dass_score),
n_subjects = n()
)
OUTPUT
# A tibble: 5 × 3
education mean_mean_dass_score n_subjects
<int> <dbl> <int>
1 0 2.38 515
2 1 2.64 4066
3 2 2.47 15066
4 3 2.31 15120
5 4 2.18 5008Now we are able to start learning interesting things about our data. For example, it seems like the average DASS score declines with higher levels of education (excluding the “missing” education = 0).
You can group by as many columns as you like. Summary statistics will then be computed for each combination of the columns:
R
dass_data %>%
group_by(education, urban) %>%
summarize(
mean_mean_dass_score = mean(mean_dass_score),
n_subjects = n()
)
OUTPUT
`summarise()` has grouped output by 'education'. You can override using the
`.groups` argument.OUTPUT
# A tibble: 20 × 4
# Groups: education [5]
education urban mean_mean_dass_score n_subjects
<int> <int> <dbl> <int>
1 0 0 2.31 19
2 0 1 2.48 126
3 0 2 2.35 141
4 0 3 2.35 229
5 1 0 2.66 49
6 1 1 2.69 626
7 1 2 2.59 1578
8 1 3 2.66 1813
9 2 0 2.62 156
10 2 1 2.46 3110
11 2 2 2.43 4720
12 2 3 2.50 7080
13 3 0 2.29 118
14 3 1 2.31 3351
15 3 2 2.28 5162
16 3 3 2.33 6489
17 4 0 2.18 40
18 4 1 2.19 1105
19 4 2 2.14 1631
20 4 3 2.20 2232Similar to count() you can also include comparisons in
group_by()
R
dass_data %>%
group_by(testelapse < 600) %>%
summarize(
mean_mean_dass_score = mean(mean_dass_score),
n_subjects = n()
)
OUTPUT
# A tibble: 2 × 3
`testelapse < 600` mean_mean_dass_score n_subjects
<lgl> <dbl> <int>
1 FALSE 2.33 2533
2 TRUE 2.39 37242Warning: Remember to
ungroup()
R remembers the group_by() function as long as you don’t
use ungroup() or summarize(). This may have
unwanted side-effects. Make sure that each group_by() is
followed by an ungroup().
R
dass_data %>%
group_by(education) %>%
mutate(
within_education_id = row_number()
) %>%
ungroup()
OUTPUT
# A tibble: 39,775 × 175
Q1A Q1I Q1E Q2A Q2I Q2E Q3A Q3I Q3E Q4A Q4I Q4E Q5A
<int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 4 28 3890 4 25 2122 2 16 1944 4 8 2044 4
2 4 2 8118 1 36 2890 2 35 4777 3 28 3090 4
3 3 7 5784 1 33 4373 4 41 3242 1 13 6470 4
4 2 23 5081 3 11 6837 2 37 5521 1 27 4556 3
5 2 36 3215 2 13 7731 3 5 4156 4 10 2802 4
6 1 18 6116 1 28 3193 2 2 12542 1 8 6150 3
7 1 20 4325 1 34 4009 2 38 3604 3 40 4826 4
8 1 34 4796 1 9 2618 1 39 5823 1 12 6596 3
9 4 4 3470 4 14 2139 3 1 11043 4 20 1829 3
10 3 38 5187 2 28 2600 4 9 2015 1 7 3111 4
# ℹ 39,765 more rows
# ℹ 162 more variables: Q5I <int>, Q5E <int>, Q6A <int>, Q6I <int>, Q6E <int>,
# Q7A <int>, Q7I <int>, Q7E <int>, Q8A <int>, Q8I <int>, Q8E <int>,
# Q9A <int>, Q9I <int>, Q9E <int>, Q10A <int>, Q10I <int>, Q10E <int>,
# Q11A <int>, Q11I <int>, Q11E <int>, Q12A <int>, Q12I <int>, Q12E <int>,
# Q13A <int>, Q13I <int>, Q13E <int>, Q14A <int>, Q14I <int>, Q14E <int>,
# Q15A <int>, Q15I <int>, Q15E <int>, Q16A <int>, Q16I <int>, Q16E <int>, …
count() + group_by()
You can also make use of group_by() to compute relative
frequencies in count-tables easily. For example, counting the education
status by handedness is not informative.
R
dass_data %>%
filter(hand %in% c(1, 2)) %>% # Focus on right vs. left
count(hand, education)
OUTPUT
hand education n
1 1 0 434
2 1 1 3481
3 1 2 13102
4 1 3 13326
5 1 4 4399
6 2 0 57
7 2 1 412
8 2 2 1623
9 2 3 1561
10 2 4 518This just tells us that there are more right-handed people. However, the percentage of education by handedness would be much more interesting. To that end, we can group by handedness and compute the percentage easily.
R
dass_data %>%
filter(hand %in% c(1, 2)) %>% # Focus on right vs. left
count(hand, education) %>%
group_by(hand) %>%
mutate(percentage = n / sum(n)) %>%
mutate(percentage = round(percentage*100, 2)) %>%
ungroup()
OUTPUT
# A tibble: 10 × 4
hand education n percentage
<int> <int> <int> <dbl>
1 1 0 434 1.25
2 1 1 3481 10.0
3 1 2 13102 37.7
4 1 3 13326 38.4
5 1 4 4399 12.7
6 2 0 57 1.37
7 2 1 412 9.88
8 2 2 1623 38.9
9 2 3 1561 37.4
10 2 4 518 12.4 As you can see, group_by() also works in combination
with mutate(). This means that calling sum(n)
computes the total of n for the given group. Which is why
n / sum(n) yields the relative frequency.
Challenges
Challenge 1
How many of the subjects voted in a national election last year? Use
count() to receive an overview.
Challenge 2
Get an overview of the number of people that voted based on whether their age is greater than 30. Make sure you only include subjects that are older than 18 in this count.
Challenge 3
Get a summary of the mean score on the DASS by whether somebody voted in a national election in past year. What do you notice? Why might this be the case? There are no right or wrong answers here, just think of some reasons.
Challenge 4
Get a summary of the mean DASS score based on age groups. Use
mutate() and case_when() to define a new
age_group column with the following cutoffs.
- < 18 = “underage”
- 18 - 25 = “youth”
- 26 - 35 = “adult”
- 36 - 50 = “middle-age”
- 50+ = “old”
R
dass_data %>%
mutate(
age_group = case_when(
age < 18 ~ "underage",
age >= 18 & age <= 25 ~ "youth",
age >= 26 & age <= 35 ~ "adult",
age >= 36 & age <= 50 ~ "middle-age",
age > 50 ~ "old"
)
) %>%
group_by(age_group) %>%
summarize(
mean_score = mean(mean_dass_score),
n_entries = n()
)
OUTPUT
# A tibble: 5 × 3
age_group mean_score n_entries
<chr> <dbl> <int>
1 adult 2.23 6229
2 middle-age 2.10 2288
3 old 1.97 950
4 underage 2.64 7269
5 youth 2.40 23039Challenge 5
Compute the relative frequency of whether somebody voted or not based on their age group (defined above). Make sure you compute the percentage of people that voted given their age, not the percentage of age given their voting status.
Challenge 6
Sort the above output by the relative frequency of people that voted.
Use the arrange() function after counting and computing the
percentage. Use the help ?arrange, Google or ChatGPT to
figure out how to use this function.
R
dass_data %>%
filter(voted > 0) %>% # Don't include missing values of voted = 0
mutate(
age_group = case_when(
age < 18 ~ "underage",
age >= 18 & age <= 25 ~ "youth",
age >= 26 & age <= 35 ~ "adult",
age >= 36 & age <= 50 ~ "middle-age",
age > 50 ~ "old"
)
) %>%
count(age_group, voted) %>%
group_by(age_group) %>%
mutate(
percentage = round(n / sum(n) * 100, 2)
) %>%
arrange(desc(percentage))
OUTPUT
# A tibble: 10 × 4
# Groups: age_group [5]
age_group voted n percentage
<chr> <int> <int> <dbl>
1 underage 2 7125 98.9
2 youth 2 17534 76.7
3 old 1 573 61.8
4 adult 1 3710 60.0
5 middle-age 1 1346 59.4
6 middle-age 2 919 40.6
7 adult 2 2468 40.0
8 old 2 354 38.2
9 youth 1 5337 23.3
10 underage 1 82 1.14Challenge 6 (continued)
What can you learn by the output?
Key Points
- Use
count()to compute the number of occurrences for (combinations) of columns - Use
summarize()to compute any summary statistics for your data - Use
group_by()to group your data so you can receive summaries for each group separately - Combine functions like
filter(),group_by()andsummarize()using the pipe to receive specific results